The present invention relates to an image processing apparatus which can reproduce, from an input image in which noise exists, the intrinsic structure of a sensed object included in the input image and, more specifically, to an image processing apparatus which performs image processing required for pre-processing for the recognition of each of a plurality of sensed objects included in one image, personal authentication using fingerprints and irises, and the like.
In general, noise is removed from an image by using the differences in luminance value between a noise pixel and neighboring pixels in consideration of the fact that the noise pixel solely has a luminance value different from those of the neighboring pixels. The smoothing filter method is a typical method of removing such isolated points from a background image and filling holes (isolated points) in a graphic pattern (object image). In this method, the average luminance value of 3×3 neighboring pixels around a pixel of interest is set as the luminance value of the pixel of interest. The method, however, has the drawback of blurring even edges. A method using a median filter which set, as the luminance value of a pixel of interest, the median value of the luminance values of 3×3 neighboring pixels around the pixel of interest is available as a method of removing noise without blurring edges (T. S. Huang, G. J. Yang, and G. Y. Tang, “A fast two-dimensional median filtering algorithm”, PRIP' 78, pp. 121–131, 1978).
There is a method of removing point noise from a background image or holes from an object image by performing erosion processing and dilation processing, which are functions of Morphology, with respect to an image object. A method of removing noise by repeating this processing is also available (J. Serra, “Image Analysis and Mathematical Morphology,” Academic Press, London, 1982, and P. Maragos, “Tutorial on advances in morphological image processing and analysis”, Opt, Eng., 26, 1987).
According to this method, letting X be an image to be processed and B be a structuring element, when (X+B) is defined as dilation of X by B and (X−B) is defined as erosion of X by B, opening and closing are defined by equations (1) and (2), respectively. Noise is removed by repeatedly performing opening and closing.XoB=(X−B)+B   (1)X·B=(X+B)−B   (2)
Assume that in erosion processing, a pixel group obtained by shifting an image in several directions on a pixel basis is overlaid on the original pixels, and the logical AND (the minimum value in the case of a halftone image) of the luminance values between these pixels is calculated, whereas in dilation processing, the logical OR (the maximum value in the case of a halftone image) of the luminance values between these pixels is calculated. In this case, when opening processing in which dilation processing is performed after erosion processing and closing processing in which erosion processing is performed after dilation processing are consecutively performed, fine noise is removed by the opening processing first, and then holes (noise) in an object image can be filled by the closing processing.
There is another example of the method of repeating erosion processing and dilation processing (R. M. Haralick, S. R. Sternber and X. Zhuang, “Image Analysis Using Mathematical Morphology”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-9, no. 4, pp. 532–550, July 1987).
In the above method (to be referred to as the first conventional method), high-speed processing can be done by using parallel hardware on a relatively small scale. However, disconnection or connection locally occurs in a processed object image, resulting in the failure of the reproduction of the structure of the object.
As a method (to be referred to as the second conventional method) of improving this, an image processing method based on the consideration of the structure of a sensed object is available. One of such methods is a noise removing method based on the edge directions of an object (M. Nagao and T. Mtusyama, “Edge preserving smoothing”, CGIP, vol. 9, pp. 394–407, April 1979). In this method, consideration is given to edge directions in such a manner that in the area of neighboring 5×5 pixels around a pixel of interest, the edge directions of an object are classified according to nine different window patterns, the variances of luminance values in the respective patterns are then obtained, and the average luminance value in the pattern exhibiting the minimum variance is set as the luminance value of the pixel of interest.
In addition, as a method specialized for an object to be sensed, the Mehtre method (B. M. Mehtre, “Fingerprint Image Analysis for Automatic Identification”, Machine Vision and Applications, vol. 6, no. 2–3, pp. 124–139, 1993) is available, which is a registration image forming algorithm in fingerprint authentication. In this method, the ridge direction of a fingerprint is obtained, and a cotextual filter is convoluted to emphasize the ridge. In the above second conventional method, the structure of an object (a ridge of a fingerprint in the latter case) in a processed image is robust. However, complicated filter processing using pixel information in a relatively large area is required, and hence the processing amount is large. This makes it difficult to realize high-speed, high-precision processing by using an inexpensive, simple apparatus.